Alzheimer’s disease (AD) is a neurodegenerative disorder, the most common form of dementia, characterized by gradual loss of cognitive function, which leads to a significant burden on society. We review and discuss how ML methods have transformed AD biomarker discovery with a specific focus on multimodal approaches which have bridged different data modalities (e.g., blood plasma, cerebrospinal fluid (CSF), and neuroimaging) through ML methods. We found numerous ML algorithms applicable to high-dimensional data that uncover subtle signatures, revealing new biomarkers for enhanced diagnostic performance including supervised, unsupervised, and deep learning models. One of the such important advances is integration of different modalities data which helps to better understand AD pathophisiology and chances to improve the specificity of AD early detection initiatives. In addition, as a new huge area of research, this manuscript also highlighted several areas of challenges that can slow translation of ML-based discoveries in the clinical settings, such as data heterogeneity, model interpretability, and clinical validation. It stresses that ML equations should be relevant to different populations, and that ethical considerations in their development and implementation are needed, all of which take interaction between disciplines. By synthesizing recent advances while noting outstanding opportunities, we describe the potential of ML to enhance AD research by enabling early diagnosis, monitoring disease progression, and offering customized treatment paradigms. Future approaches will include integrating new technologies such as multiplexed biomarker detection and novel data sources from wearables into diagnostic pipelines and progressing toward the clinical implementation of ML methods for treating AD.